Instructions to use sphaela/Qwen3.6-27B-AutoRound-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with Ollama:
```
ollama run hf.co/sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
```
Unsloth Desktop

How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with Docker Model Runner:
```
docker model run hf.co/sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M
```

Lemonade

How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3.6-27B-AutoRound-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use sphaela/Qwen3.6-27B-AutoRound-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "sphaela/Qwen3.6-27B-AutoRound-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

sphaela commited on Apr 24

Commit

4bd9a6a

verified ·

1 Parent(s): 6b210f6

Update README.md

Browse files

Add description for mixed quant based on discussion reported result.

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -24,7 +24,7 @@ The models were quantized using various schemes provided by the `auto-round` too
 | File Name | Quant Type | Size | Description |
 |-----------|------------|------|-------------|
 | `Qwen3.6-27B-Q2_K_S.gguf` | Q2_K_S | 8.9 GB | Extremely high compression, significant quality loss. |
-| `Qwen3.6-27B-Q2_K_MIXED.gguf` | Q2_K_MIXED | 16 GB | **Recommended** high-compression option. Uses Q4 for KV cache with good quality. |
 | `Qwen3.6-27B-Q3_K_S.gguf` | Q3_K_S | 12 GB | Very high compression, notable quality loss. |
 | `Qwen3.6-27B-Q3_K_M.gguf` | Q3_K_M | 12 GB | Balanced 3-bit quantization. |
 | `Qwen3.6-27B-Q3_K_L.gguf` | Q3_K_L | 12 GB | High quality 3-bit quantization. |

 | File Name | Quant Type | Size | Description |
 |-----------|------------|------|-------------|
 | `Qwen3.6-27B-Q2_K_S.gguf` | Q2_K_S | 8.9 GB | Extremely high compression, significant quality loss. |
+| `Qwen3.6-27B-Q2_K_MIXED.gguf` | Q2_K_MIXED | 16 GB | **Recommended** high-compression option. Uses Q4 for KV cache with good quality. *Fast inference*.|
 | `Qwen3.6-27B-Q3_K_S.gguf` | Q3_K_S | 12 GB | Very high compression, notable quality loss. |
 | `Qwen3.6-27B-Q3_K_M.gguf` | Q3_K_M | 12 GB | Balanced 3-bit quantization. |
 | `Qwen3.6-27B-Q3_K_L.gguf` | Q3_K_L | 12 GB | High quality 3-bit quantization. |